
Veo 3.1 | Reference to Video
A faster, lightweight version of the first-last frame model. Ideal for quick prototypes or test scenes requiring smooth transitions.
Avg Run Time: 100.000s
Model Slug: veo3-1-reference-to-video
Release Date: October 15, 2025
Category: Image to Video
Input
Output
Example Result
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Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Overview
Veo 3.1 Reference-to-Video is a state-of-the-art AI video generation model developed by Google DeepMind, designed to transform up to three reference images and a text prompt into high-fidelity, short video clips with smooth transitions and, optionally, synchronized audio. It is positioned as a faster, lightweight variant of the first-last frame animation approach, making it especially suitable for rapid prototyping, test scenes, and scenarios where smooth transitions between keyframes are essential. The model leverages advanced generative architecture to interpret visual and textual inputs, producing realistic motion, consistent character appearance, and coherent scene composition. What sets Veo 3.1 apart is its ability to preserve artistic style and subject fidelity across frames, support for native audio generation, and enhanced control over cinematic elements such as camera motion and ambiance. This makes it a powerful tool for both creative professionals and technical users seeking to automate or accelerate video content creation with a high degree of visual and narrative control.
Technical Specifications
- Architecture: Google DeepMind Veo 3.1 (exact architecture details not publicly disclosed, but based on next-generation generative video models)
- Parameters: Not publicly specified
- Resolution: 720p or 1080p output
- Duration: Typically 4, 6, or 8 seconds per clip (with some sources noting up to 60 seconds in extended scenarios)
- Frame rate: 24 fps
- Aspect ratio: 16:9 or 9:16
- Input formats: Up to 3 reference images (JPEG/PNG, up to 8MB each), text prompt
- Output formats: MP4 video
- Audio: Optional native audio generation, synchronized with video
- Performance metrics: High visual realism, strong scene coherence, and audio-visual synchronization reported in technical comparisons
Key Considerations
- Prepare clear, concise prompts that describe subject, action, camera, style, and environment for best results.
- Use up to three high-quality reference images to guide character or object consistency—poor-quality or inconsistent references can degrade output quality.
- Review generated clips for subject fidelity, motion, framing, lighting, and audio alignment; iterate on prompts and references as needed.
- Be aware of the trade-off between generation speed and output quality; this model is optimized for quick prototypes, so complex or highly detailed scenes may require multiple iterations.
- Frame-specific control (e.g., constraining first/last frames) can help achieve desired transitions, but may not be supported in all implementations.
- Audio generation increases computational cost and may affect pricing in some deployment scenarios.
- Safety filters are applied to both input images and generated content to prevent misuse.
Tips & Tricks
- For smooth transitions, structure your prompt to explicitly describe how the scene should animate between the first and last reference image.
- Experiment with different camera motions and ambiance descriptions in your prompt to achieve varied cinematic effects.
- If the output lacks consistency, try refining the reference images or adding more descriptive details to the prompt.
- Use the iterative workflow: generate, review, refine prompt/images, and regenerate to progressively improve results.
- For longer videos, generate multiple clips and stitch them together in a video editor.
- Leverage frame-specific controls where available to lock down key moments in the animation.
- To save costs, consider generating shorter clips or disabling audio when prototyping.
Capabilities
- Generates high-fidelity, realistic videos from text prompts and reference images, with smooth transitions between keyframes.
- Preserves subject appearance and artistic style across frames using reference images.
- Supports native, synchronized audio generation for a more immersive output.
- Offers control over cinematic elements such as camera motion, lighting, and ambiance via prompt engineering.
- Enables rapid prototyping and iterative refinement, making it suitable for both creative and technical workflows.
- Delivers strong scene coherence and character continuity, even in multi-shot sequences.
- Suitable for generating both landscape (16:9) and portrait (9:16) aspect ratio videos.
What Can I Use It For?
- Quick prototyping of animated scenes for film, advertising, or game development, where smooth transitions between key poses or scenes are needed.
- Creating product demo videos by animating between different states or angles of a product, guided by reference images.
- Generating animated storyboards or pre-visualizations for directors and animators, enabling rapid iteration on visual concepts.
- Producing social media content, such as animated avatars, stylized transitions, or short narrative clips, with consistent character appearance.
- Educational content creation, such as explainer videos with animated diagrams or characters.
- Automated video content for e-commerce, showcasing products in different configurations or environments.
- Experimental and artistic projects exploring the intersection of AI and cinema, as documented in creative tech blogs and forums.
Things to Be Aware Of
- User feedback highlights the model’s strength in visual realism and scene coherence, especially compared to earlier versions and some competitors.
- The model is praised for its ability to handle multi-scene transitions and maintain character consistency, which is valuable for narrative projects.
- Some users note that while the model is fast for prototyping, achieving highly detailed or complex scenes may require multiple iterations and careful prompt engineering.
- Audio generation, while impressive, can significantly increase the cost per second of video in some deployment scenarios.
- There is a learning curve to effective prompt and reference image selection; suboptimal inputs can lead to inconsistent or lower-quality outputs.
- The model applies safety filters to inputs and outputs, which may restrict certain types of content.
- Community discussions suggest that the model’s performance is best for short to medium-length clips; very long or highly dynamic scenes may challenge its coherence.
- Positive reviews often mention the ease of integrating the model into iterative creative workflows, but some users desire even finer control over motion and timing.
Limitations
- Output duration is typically limited to short clips (commonly 4–8 seconds, with some extensions possible), which may not suit all narrative or commercial needs.
- Highly complex or fast-paced scenes can sometimes result in less coherent motion or artifacts, requiring manual refinement.
- The model’s performance and quality depend heavily on the quality and relevance of reference images and the precision of the text prompt.
- Native audio generation, while advanced, may not always perfectly match the desired mood or pacing of the visual content.
- As with most generative models, there is a risk of unintended biases or artifacts in the output, necessitating careful review before final use.
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